Papers by Gianni Barlacchi
Neural Ranking with Weak Supervision for Open-Domain Question Answering : A Survey (2023.findings-eacl)
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| Challenge: | Neural ranking models require substantial amounts of relevance annotations, which is costly to scale. |
| Approach: | They propose to train a NR model with weak supervision instead of annotations . they use a structured overview of standard WS signals used for training a model . |
| Outcome: | The proposed approach reduces the cost of annotations by using weak supervision instead of a parametric model. |
FocusQA: Open-Domain Question Answering with a Context in Focus (2022.findings-emnlp)
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Gianni Barlacchi, Ivano Lauriola, Alessandro Moschitti, Marco Del Tredici, Xiaoyu Shen, Thuy Vu, Bill Byrne, Adrià de Gispert
| Challenge: | a new method for question answering with a context in focus simulates a free interaction with QA systems. |
| Approach: | They introduce question answering with a cotext in focus task that simulates a free interaction with QA systems. |
| Outcome: | The proposed model outperforms state-of-the-art models for question answering with a context in focus up to 21.3% absolute points. |
Who is the richest club in the championship? Detecting and Rewriting Underspecified Questions Improve QA Performance (2026.findings-acl)
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| Challenge: | Large language models (LLMs) perform well on well-posed factual queries, yet standard question-answering (QA) benchmarks remain far from solved. |
| Approach: | They propose an LLM-based classifier to identify underspecified questions and apply it to several widely used QA datasets. |
| Outcome: | The proposed classifier detects underspecified questions in QA datasets and significantly improves on them. |
Strong and Efficient Baselines for Open Domain Conversational Question Answering (2023.findings-emnlp)
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| Challenge: | Current Open Domain Question Answering (ODQA) approaches to reevaluating baselines for efficiency and effectiveness have been limited due to various limitations. |
| Approach: | They propose and evaluate strong yet simple and efficient baselines by introducing a fast reranking component between the retriever and the reader and performing targeted finetuning steps. |
| Outcome: | The proposed method improves the SotA results while reducing the reader’s latency by 60%. |